潜在有毒元素（PTEs）在大多数地质材料中天然存在。然而，近期人为干扰，如矿石开采也显著增加了土壤中这些元素的富集。它们在土壤中的存在可能给环境和生物带来各种相关风险。大多数传统土壤质量评价方法涉及将元素的背景值与既定的指导值进行比较，这经常耗时且易出现计算错误。因此，为了对土壤质量及其对生态系统和人体健康的影响进行全面和无偏见的评价，本研究对整个研究区域进行了综合地球化学数据、数值和地理信息系统数据的健康风险综合区分。此外，本研究采用多层感知器人工神经网络（MLP-NN）预测对土壤质量影响最重要的有毒成分。地球化学、统计学和定量土壤污染评价（污染指数和生态风险指数）显示，除了矿山开采，微量元素和氧化物的扩散和关联也是表面环境条件（如淋洗、风化和有机金属络合）的结果。所有PTEs的危害商数（HQs）和危险指数（HI）都大于一。这表明居民（尤其是儿童）摄入有毒元素的风险比皮肤暴露和吸入更高。摄入As和Cr导致更高的癌症风险和终身癌症风险水平（> 1.0E 04），其中风险水平向研究区域的东北、西部和东南方向增加。从平方误差和相对误差以及确定系数的低建模误差水平来看，MLP-NN在污染负荷预测中的效率得到了确认。根据敏感性分析，Hg、Sr、Zn、Ba、As和Zr对土壤质量的影响最大，因此应注重从土壤中去除这些元素。©2023. 作者独家许可给 Springer Nature B.V.。

Potentially toxic elements (PTEs) occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational errors. As a result, to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the entire study area. Furthermore, the multilayer perceptron artificial neural network (MLP-NN) was used to forecast the most important toxic components influencing soil quality. Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic element ingestion than dermal exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing toward the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil.© 2023. The Author(s), under exclusive licence to Springer Nature B.V.